#SGPMD-NMF
trace_pmd = io.imread('/home/nel/Code/volpy_test/invivo-imaging/test_data/09282017Fish1-1/output/temporal_traces.tif')[1]
trace_pmd = np.concatenate((np.array([0]*100), trace_pmd))[:len(frame_timing)]
trace_pmd = signal_filter(trace_pmd, freq=15, fr=400)
trace_pmd = trace_pmd / np.max(trace_pmd)
plt.plot(frame_timing, trace_pmd, label='pmd',color='red')
spikes_pmd = scipy.signal.find_peaks(trace_pmd, 0.45, distance=5)[0]
plt.plot(frame_timing[spikes_pmd], np.max(ephys) * 1.3 * np.ones(spikes_pmd.shape),
color='red', marker='.', fillstyle='none', linestyle='none')
plt.legend()
"""
#%%
import caiman as cm
from caiman.base.rois import nf_read_roi_zip
masks = nf_read_roi_zip(os.path.join(root_dir, 'mask.zip'), dims=(44, 128))
masks_m = masks[0].reshape(-1, order='F')
m = cm.load('/home/nel/data/voltage_data/simul_electr/johannes/09282017Fish1-1/memmap__d1_44_d2_128_d3_1_order_C_frames_37950_.mmap')
mm = m.reshape((m.shape[0], -1), order='F')
trace_mean = (mm[:, masks_m>0]).mean(1)
trace_mean = signal_filter(-trace_mean[np.newaxis, :], freq=1/3, fr=300)[0]
trace_mean = signal_filter(trace_mean[np.newaxis, :], freq=10, fr=300)[0]
trace_mean = trace_mean / trace_mean.max()
plt.plot(trace_mean)
spikes_mean = scipy.signal.find_peaks(trace_mean, 0.3, distance=5)[0]
spikes_mean = np.delete(spikes_mean, np.where(spikes_mean>len(frame_timing))).astype(np.int32)
#%%
import caiman as cm
from caiman.base.rois import nf_read_roi_zip
masks = nf_read_roi_zip(os.path.join(root_dir, 'mask.zip'), dims=(32, 64))
#%%
for folder_out in folders_out:
projection_img_median = folder_out + '/projections/median_projection.tif'
projection_img_correlation = folder_out + '/projections/correlation_image.tif'
folder_in = folder_out + '/regions'
performance_all = dict()
fls = list(glob.glob(folder_in + '/*_nd.zip'))
consensus_counter = dict()
fl1 = fls[0]
for fl2 in fls[1:]:
print([fl1, fl2])
Cn = cm.load(projection_img_correlation)
shape = Cn.shape
roi_1, names_1 = nf_read_roi_zip(fl1, shape, return_names=True)
roi_2, names_2 = nf_read_roi_zip(fl2, shape, return_names=True)
# pl.figure()
# pl.imshow(np.sum(roi_1,0),cmap = 'gray',vmax=2,alpha=.5)
# pl.imshow(np.sum(roi_2,0),cmap = 'hot',vmax=2,alpha=.5)
lab1, lab2 = fl1.split('/')[-1][:-4], fl2.split('/')[-1][:-4]
# pl.figure(figsize=(15,10))
tp_gt, tp_comp, fn_gt, fp_comp, performance = cm.base.rois.nf_match_neurons_in_binary_masks(
roi_1,
roi_2,
thresh_cost=.7,
min_dist=10,
print_assignment=False,
plot_results=False,
Cn=Cn,
labels=[lab1, lab2])
'regions/sonia_all_regions.zip', 'regions/ben_all_regions.zip'
]
results = dict()
for count, par in enumerate(params):
print(
os.path.join('/mnt/ceph/neuro/labeling/', par[0],
'projections/correlation_image.tif'))
c_img = cm.load(
os.path.join('/mnt/ceph/neuro/labeling/', par[0],
'projections/correlation_image.tif'))
result = dict()
for region_pairs in itertools.combinations(label_name, 2):
print(region_pairs)
try:
roi_nat = nf_read_roi_zip(
os.path.join('/mnt/ceph/neuro/labeling/', par[0],
region_pairs[0]), c_img.shape)
roi_lin = nf_read_roi_zip(
os.path.join('/mnt/ceph/neuro/labeling/', par[0],
region_pairs[1]), c_img.shape)
print(roi_nat.shape)
print(roi_lin.shape)
# roi_nat = nf_read_roi_zip(os.path.join('/mnt/ceph/neuro/labeling/',par[0],'regions/natalia_all_regions.zip'),c_img.shape)
# roi_lin = nf_read_roi_zip(os.path.join('/mnt/ceph/neuro/labeling/',par[0],'regions/lindsey_all_regions.zip'),c_img.shape)
except:
print('******************** PROBLEMS WITH ' + par[0])
continue
#
# pl.subplot(3,2,count+1)
# c_img[np.isnan(c_img )] = 0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 14 09:42:20 2017
@author: agiovann
"""
from caiman.base.rois import nf_read_roi_zip
# data transformation because of motion correction mismatch in shape
#%% sue k37
new_templ = cm.load('projections/median_projection.tif')
regions = nf_read_roi_zip('regions/joined_consensus_active_regions.zip',
new_templ.shape)
new_templ = np.pad(new_templ.T, ((7, 8), (4, 3)),
mode='constant',
constant_values=new_templ.mean())
regions = np.pad(regions.transpose(0, 2, 1), ((0, 0), (7, 8), (4, 3)),
mode='constant')
np.save('regions/joined_consensus_active_regions', regions)
np.save('projections/median_projection', new_templ)
#%% if no change
corr_img = cm.load('projections/correlation_image.tif')
new_templ = cm.load('projections/median_projection.tif')
regions = nf_read_roi_zip('regions/joined_consensus_active_regions.zip',
new_templ.shape)
pl.imshow(new_templ / np.nanmax(new_templ), cmap='gray', vmin=0.01, vmax=.3)
#pl.imshow(corr_img/np.nanmax(corr_img),cmap = 'gray',vmax = .25)
pl.imshow(A_on_thr.sum(-1).reshape(dims_on, order='F'))
#%%
roi_cons = np.load(
'/mnt/ceph/neuro/labeling/neurofinder.02.00/regions/joined_consensus_active_regions.npy'
)
print(roi_cons.shape)
pl.imshow(roi_cons.sum(0))
#%%
roi_cons = np.load(
'/mnt/ceph/neuro/labeling/neurofinder.03.00.test/regions/joined_consensus_active_regions.npy'
)
print(roi_cons.shape)
pl.imshow(roi_cons.sum(0))
#%%
roi_cons = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/neurofinder.02.00/regions/ben_active_regions_nd_natalia_active_regions_nd__sonia_active_regions_nd__lindsey_active_regions_nd_matches.zip',
Cn.shape)
print(roi_cons.shape)
#%% SUE
roi_cons = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/k53_20160530/regions/ben_active_regions_nd_natalia_active_regions_nd__sonia_active_regions_nd__lindsey_active_regions_nd_matches.zip',
Cn.shape)
print(roi_cons.shape)
#%%
roi_cons = np.load(
'/mnt/ceph/neuro/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'
)
#roi_cons = np.load('/mnt/ceph/neuro/labeling/k53_20160530/regions/joined_consensus_active_regions.npy')
print(roi_cons.shape)
#%%
roi_cons = nf_read_roi_zip(
v = np.load(os.path.join(path, files), allow_pickle=True).item()
v_temporal = v['ts'][v_idx][:len(frame_timing)]
v_temporal = v_temporal - np.mean(v_temporal)
v_spikes = v['spikes'][v_idx].copy()
v_spikes = np.delete(v_spikes,
np.where(v_spikes > len(frame_timing))).astype(np.int32)
v_spatial = v['weights'][v_idx].copy()
v_temporal = v_temporal / np.max(v_temporal)
plt.figure()
plt.plot(v_temporal)
plt.figure()
plt.imshow(v_spatial)
# Mean ROI result
folder = os.path.join(ROOT_FOLDER, name)
masks = nf_read_roi_zip(os.path.join(folder, 'mask.zip'), dims=dims)
masks_m = masks[0].reshape(-1, order='F')
mov_path = os.listdir(os.path.join(folder, 'volpy'))
mov_path = [p for p in mov_path if 'F_frames' in p][0]
m = cm.load(os.path.join(folder, 'volpy', mov_path))
mm = m.reshape((m.shape[0], -1), order='F')
m_temporal = (mm[:, masks_m > 0]).mean(1)
m_temporal = signal_filter(-m_temporal[np.newaxis, :], freq=15, fr=fr)[0]
m_temporal = m_temporal / m_temporal.max()
m_spikes = scipy.signal.find_peaks(m_temporal,
m_height,
distance=int(fr * 0.01))[0]
m_spikes = np.delete(m_spikes,
np.where(m_spikes > len(frame_timing))).astype(np.int32)
plt.plot(m_temporal)
'/mnt/ceph/neuro/labeling/neurofinder.04.00.test']
foldernames = ['/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/neurofinder.01.01',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/packer.001',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/Yi.data.001',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/yuste.Single_150u',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/Jan-AMG1_exp2_new_001']
#%%
import glob
from caiman.base.rois import detect_duplicates, nf_merge_roi_zip, nf_read_roi_zip
from shutil import copyfile
#%%
for fldname in foldernames:
current_folder = os.path.join(
'/mnt/ceph/neuro/labeling', fldname, 'regions')
img_shape = cm.load(os.path.join('/mnt/ceph/neuro/labeling',
fldname, 'projections/correlation_image.tif')).shape
filenames = glob.glob(os.path.join(current_folder, '*active*regions.zip'))
for flname in filenames:
ind_dup, ind_keep = detect_duplicates(flname, 0.25, FOV=img_shape)
rois = nf_read_roi_zip(flname, img_shape)
new_fname = flname[:-4] + '_nd.zip'
print(flname)
if not ind_dup:
copyfile(flname, new_fname)
else:
nf_merge_roi_zip([flname], [ind_dup], flname[:-4] + '_copy')
nf_merge_roi_zip([flname], [ind_keep], new_fname[:-4])
print('FOUND!!')
print(np.mean(m, axis=(0,1)))
a.append(np.mean(m, axis=(0,1)))
#%% Save mask from .zip to json
from caiman.base.rois import nf_read_roi_zip
from caiman.base.rois import nf_masks_to_json
img_dir = '/home/nel/data/voltage_data/volpy_paper/img'
zip_dir = '/home/nel/data/voltage_data/volpy_paper/zip'
mask_dir = '/home/nel/data/voltage_data/volpy_paper/mask'
fls = os.listdir(img_dir)
for fi in fls:
img_path = os.path.join(img_dir, fi)
dims = cv2.imread(img_path).shape[:2]
zip_path = os.path.join(zip_dir, fi[:-4]+'.zip')
masks = nf_read_roi_zip(zip_path, dims=dims)
mask_path = os.path.join(mask_dir, fi[:-4]+'.json')
nf_masks_to_json(masks, os.path.join(mask_path))
#%% Zip to json
dr = '/home/nel/data/voltage_data/volpy_paper/img'
dr_mask = '/home/nel/data/voltage_data/volpy_paper/mask'
files = sorted(os.listdir(dr))
files = [file for file in files if '.npz' in file]
for file in files:
m = np.load(os.path.join(dr, file), allow_pickle=True)['arr_0']
dims = m.shape[:2]
mask = nf_read_roi_zip(os.path.join(dr_mask, file[:-4]+'.zip'), dims=dims)
'/mnt/ceph/neuro/labeling/neurofinder.04.00.test']
foldernames = ['/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/neurofinder.01.01',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/packer.001',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/Yi.data.001',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/yuste.Single_150u',
'/mnt/ceph/neuro/labeling/FINAL_NO_USED_FOR_CONSENSUS/Jan-AMG1_exp2_new_001']
#%%
import glob
from caiman.base.rois import detect_duplicates, nf_merge_roi_zip, nf_read_roi_zip
from shutil import copyfile
#%%
for fldname in foldernames:
current_folder = os.path.join(
'/mnt/ceph/neuro/labeling', fldname, 'regions')
img_shape = cm.load(os.path.join('/mnt/ceph/neuro/labeling',
fldname, 'projections/correlation_image.tif')).shape
filenames = glob.glob(os.path.join(current_folder, '*active*regions.zip'))
for flname in filenames:
ind_dup, ind_keep = detect_duplicates(flname, 0.25, FOV=img_shape)
rois = nf_read_roi_zip(flname, img_shape)
new_fname = flname[:-4] + '_nd.zip'
print(flname)
if not ind_dup:
copyfile(flname, new_fname)
else:
nf_merge_roi_zip([flname], [ind_dup], flname[:-4] + '_copy')
nf_merge_roi_zip([flname], [ind_keep], new_fname[:-4])
print('FOUND!!')
par[0], 'projections/correlation_image.tif'))
c_img = cm.load(os.path.join('/mnt/ceph/neuro/labeling/',
par[0], 'projections/correlation_image.tif'))
result = dict()
if compare_code == 0:
iterlabels = label_name
elif compare_code == 1:
iterlabels = itertools.combinations(label_name, 2)
else:
raise Exception('Not defined')
for region_pairs in iterlabels:
print(region_pairs)
try:
if compare_code == 0:
roi_nat = nf_read_roi_zip(os.path.join(
'/mnt/ceph/neuro/labeling/', par[0], consensus_name), c_img.shape)
roi_lin = nf_read_roi_zip(os.path.join(
'/mnt/ceph/neuro/labeling/', par[0], region_pairs), c_img.shape)
else:
roi_nat = nf_read_roi_zip(os.path.join(
'/mnt/ceph/neuro/labeling/', par[0], region_pairs[0]), c_img.shape)
roi_lin = nf_read_roi_zip(os.path.join(
'/mnt/ceph/neuro/labeling/', par[0], region_pairs[1]), c_img.shape)
print(roi_nat.shape)
print(roi_lin.shape)
# roi_nat = nf_read_roi_zip(os.path.join('/mnt/ceph/neuro/labeling/',par[0],'regions/natalia_all_regions.zip'),c_img.shape)
# roi_lin = nf_read_roi_zip(os.path.join('/mnt/ceph/neuro/labeling/',par[0],'regions/lindsey_all_regions.zip'),c_img.shape)
except:
print('******************** PROBLEMS WITH ' + par[0])
pl.rc('font', **font)
for folder_out in folders_out[:]:
projection_img_median = folder_out + '/projections/median_projection.tif'
projection_img_correlation = folder_out + '/projections/correlation_image.tif'
folder_in = folder_out + '/regions'
performance_all = dict()
fls = list(glob.glob(folder_in + '/*_nd.zip'))
consensus_counter = dict()
fl1 = os.path.join(folder_in, 'joined_consensus_active_regions.zip')
for fl2 in fls:
print([fl1, fl2])
Cn = cm.load(projection_img_correlation)
shape = Cn.shape
roi_1, names_1 = nf_read_roi_zip(fl1, shape, return_names=True)
roi_2, names_2 = nf_read_roi_zip(fl2, shape, return_names=True)
# pl.figure()
# pl.imshow(np.sum(roi_1,0),cmap = 'gray',vmax=2,alpha=.5)
# pl.imshow(np.sum(roi_2,0),cmap = 'hot',vmax=2,alpha=.5)
lab1, lab2 = fl1.split('/')[-1][:-4], fl2.split('/')[-1][:-4]
# pl.figure(figsize=(15,10))
tp_gt, tp_comp, fn_gt, fp_comp, performance = cm.base.rois.nf_match_neurons_in_binary_masks(roi_1, roi_2, thresh_cost=.7, min_dist=10,
print_assignment=False, plot_results=False, Cn=Cn, labels=[lab1, lab2])
performance['tp_gt'] = tp_gt
performance['tp_comp'] = tp_comp
performance['fn_gt'] = fn_gt
performance['fp_comp'] = fp_comp
performance_all[fl1, fl2] = performance
ss=None,
dview=dview)
A_on_thr = A_on_thr > 0
size_neurons = A_on_thr.sum(0)
A_on_thr = A_on_thr[:, size_neurons > min_size_neuro]
C_on_thr = C_on[size_neurons > min_size_neuro, :116000]
print(A_on_thr.shape)
C_on_thr = np.array(
[CC.reshape([-1, n_frames_per_bin]).max(1) for CC in C_on_thr])
#%%
pl.figure()
pl.imshow(A_on_thr.sum(-1).reshape(dims_on, order='F'))
#%% load labelers
roi_rs = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/k53_20160530/regions/sonia_active_regions.zip',
Cn.shape)
print(roi_rs.shape)
roi_bs = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/k53_20160530/regions/lindsey_active_regions.zip',
Cn.shape)
print(roi_bs.shape)
#roi_ds = nf_read_roi_zip('/mnt/ceph/neuro/labeling/J115_2015-12-09_L01/regions/natalia_active_regions_els.zip',Cn.shape)
#print(roi_ds.shape)
#%%
roi_rs = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/J115_2015-12-09_L01/regions/sonia_active_regions_els.zip',
Cn.shape)
print(roi_rs.shape)
roi_bs = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/J115_2015-12-09_L01/regions/lindsey_active_regions_els.zip',
import skimage
masks = np.zeros((m1.shape[0], m1.shape[1], mask_new.shape[0]))
for i, mm in enumerate(mask_new):
rr, cc = skimage.draw.polygon(mm['all_points_y'], mm['all_points_x'])
masks[rr, cc, i] = 1
plt.imshow(masks.sum(2))
plt.show()
plt.imshow(m1[:, :, 0])
#%%
save_folder = '/home/nel/Code/NEL_LAB/Mask_RCNN/datasets/voltage_v1.2_L1_0.5'
group = 'train'
np.savez(save_folder + '/' + group + '/' + file[:-4] + '_half.npz', img=m1)
np.savez(save_folder + '/' + group + '/' + file[:-4] + '_mask.npz',
mask=mask_new)
#%% transform labels to hdf5 for Viola
from caiman.base.rois import nf_read_roi_zip
folder = '/home/nel/Code/NEL_LAB/Mask_RCNN/labels/combination_v1.2'
save_folder = '/home/nel/Code/NEL_LAB/Mask_RCNN/labels/combination_v1.2_hdf5'
files = os.listdir(folder)
files = sorted(
[file for file in files if 'Fish' not in file and 'IVQ' not in file])
for file in files:
path = os.path.join(folder, file)
m = nf_read_roi_zip(path, dims=(512, 128))
cm.movie(m).save(os.path.join(save_folder, file[:-4] + '_ROIs.hdf5'))
# Johannes's ROI
roi_dir = '/home/nel/Code/Voltage_imaging/exampledata/Johannes/data/'
rname = roi_dir+ds+'/ROI_info.npy'
y = np.load(rname, allow_pickle=True)
dims = X.shape
img = np.zeros((dims[0], dims[1]))
for i in range(len(y)):
img[y[i]['pixel_yx_list'][:,0],y[i]['pixel_yx_list'][:,1]] = 1
plt.figure();plt.imshow(img)
# Read my ROI
dims = (508, 288)#(360, 256)#(364,320)
roi_dir = '/home/nel/Code/VolPy/UNet/ROIs/Johannes'
rname = roi_dir+ds+'_RoiSet.zip'
from caiman.base.rois import nf_read_roi_zip
img = nf_read_roi_zip(rname,dims)
plt.figure();plt.imshow(img.sum(axis=0))
# Form training data
npz_dir = '/home/nel/Code/VolPy/UNet/npz/Johannes/'
X_j = []
dimension = []
for index, file in enumerate(sorted(os.listdir(npz_dir))):
if file[-3:] == 'npz':
temp = np.load(npz_dir+ file)['arr_0']
dimension.append([temp.shape[0], temp.shape[1]])
temp = Mirror(temp)
X_j.append(temp)
X_j = np.array(X_j)
roi_dir = '/home/nel/Code/VolPy/UNet/ROIs/Johannes/'
for CC in C_on_thr])
#%%
pl.figure()
pl.imshow(A_on_thr.sum(-1).reshape(dims_on, order='F'))
#%%
roi_cons = np.load(
'/mnt/ceph/neuro/labeling/neurofinder.02.00/regions/joined_consensus_active_regions.npy')
print(roi_cons.shape)
pl.imshow(roi_cons.sum(0))
#%%
roi_cons = np.load(
'/mnt/ceph/neuro/labeling/neurofinder.03.00.test/regions/joined_consensus_active_regions.npy')
print(roi_cons.shape)
pl.imshow(roi_cons.sum(0))
#%%
roi_cons = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/neurofinder.02.00/regions/ben_active_regions_nd_natalia_active_regions_nd__sonia_active_regions_nd__lindsey_active_regions_nd_matches.zip', Cn.shape)
print(roi_cons.shape)
#%% SUE
roi_cons = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/k53_20160530/regions/ben_active_regions_nd_natalia_active_regions_nd__sonia_active_regions_nd__lindsey_active_regions_nd_matches.zip', Cn.shape)
print(roi_cons.shape)
#%%
roi_cons = np.load(
'/mnt/ceph/neuro/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy')
#roi_cons = np.load('/mnt/ceph/neuro/labeling/k53_20160530/regions/joined_consensus_active_regions.npy')
print(roi_cons.shape)
#%%
roi_cons = nf_read_roi_zip(
'/mnt/ceph/neuro/labeling/J115_2015-12-09_L01_ELS/regions/ben_active_regions_nd_natalia_active_regions_nd__sonia_active_regions_nd__lindsey_active_regions_nd_matches.zip', Cn.shape)
#roi_cons = np.load('/mnt/ceph/neuro/labeling/k53_20160530/regions/joined_consensus_active_regions.npy')
print(roi_cons.shape)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 14 09:42:20 2017
@author: agiovann
"""
from caiman.base.rois import nf_read_roi_zip
# data transformation because of motion correction mismatch in shape
#%% sue k37
new_templ = cm.load('projections/median_projection.tif')
regions = nf_read_roi_zip(
'regions/joined_consensus_active_regions.zip', new_templ.shape)
new_templ = np.pad(new_templ.T, ((7, 8), (4, 3)),
mode='constant', constant_values=new_templ.mean())
regions = np.pad(regions.transpose(0, 2, 1),
((0, 0), (7, 8), (4, 3)), mode='constant')
np.save('regions/joined_consensus_active_regions', regions)
np.save('projections/median_projection', new_templ)
#%% if no change
corr_img = cm.load('projections/correlation_image.tif')
new_templ = cm.load('projections/median_projection.tif')
regions = nf_read_roi_zip(
'regions/joined_consensus_active_regions.zip', new_templ.shape)
pl.imshow(new_templ / np.nanmax(new_templ), cmap='gray', vmin=0.01, vmax=.3)
frame_name = os.path.join(images_fold,
'image' + str(counter).zfill(5) + '.tif')
im = Image.fromarray(fr)
im.save(frame_name)
z.write(frame_name, os.path.basename(frame_name))
os.remove(frame_name)
counter += 1
z.close()
dims = np.shape(mov)[1:]
regionslist = glob.glob(os.path.join(regions_fold, '*'))
for region in regionslist:
if 'ben_active_regions_nd.zip' in region:
name = os.path.join(regions_dist, 'L4_regions.json')
masks = nf_read_roi_zip(region, dims)
elif 'lindsey_active_regions_nd.zip' in region:
name = os.path.join(regions_dist, 'L3_regions.json')
masks = nf_read_roi_zip(region, dims)
elif 'sonia_active_regions_nd.zip' in region:
name = os.path.join(regions_dist, 'L2_regions.json')
masks = nf_read_roi_zip(region, dims)
elif 'natalia_active_regions_nd.zip' in region:
name = os.path.join(regions_dist, 'L1_regions.json')
masks = nf_read_roi_zip(region, dims)
elif 'joined_consensus_active_regions.npy' in region:
name = os.path.join(regions_dist, 'consensus_regions.json')
masks = np.load(region)
else:
print('discarded ' + region)
continue
#%%
img_folder = '/home/nel/NEL-LAB Dropbox/NEL/Papers/VolPy/manual_annotations/summary_images/images/'
root_folder = '/home/nel/NEL-LAB Dropbox/NEL/Papers/VolPy/manual_annotations/summary_images/output/'
#combined_folder = '/home/nel/NEL-LAB Dropbox/NEL/Papers/VolPy/manual_annotations/summary_images/combination'
save_img_folder = '/home/nel/NEL-LAB Dropbox/NEL/Papers/VolPy/manual_annotations/summary_images/comparison_v1.2/'
annotators = sorted(os.listdir(root_folder))
# select = [0, 1]
for select in [[0,1], [0,2], [1,2]]:
filenames = sorted(os.listdir(root_folder+annotators[select[0]]))
result = {}
#filenames1 = sorted(os.listdir(root_folder+annotators[select[1]]))
for file in filenames:
img = cm.load(os.path.join(img_folder, file[:-4]+'_summary.tif'))[0]
dims = img.shape
mask0 = nf_read_roi_zip(os.path.join(root_folder, annotators[select[0]], file), dims=dims) * 1.
mask1 = nf_read_roi_zip(os.path.join(root_folder, annotators[select[1]], file), dims=dims) * 1.
tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = nf_match_neurons_in_binary_masks(
mask0, mask1, thresh_cost=0.7, min_dist=10, print_assignment=True,
plot_results=True, Cn=img, labels=[annotators[select[0]], annotators[select[1]]])
plt.savefig(os.path.join(save_img_folder, annotators[select[0]][0]+'&'+annotators[select[1]][0]+file[:-4]+'.pdf'))
plt.close()
result[file] = performance_cons_off
#%%
processed = {}
for i in ['f1_score','recall','precision']:
#result = eval(i)
processed[i] = {}
for j in ['L1','TEG','HPC']: